Overview

Dataset statistics

Number of variables16
Number of observations1696392
Missing cells1380084
Missing cells (%)5.1%
Duplicate rows55458
Duplicate rows (%)3.3%
Total size in memory207.1 MiB
Average record size in memory128.0 B

Variable types

Categorical5
Text7
Numeric3
DateTime1

Alerts

Dataset has 55458 (3.3%) duplicate rowsDuplicates
Relation is highly imbalanced (79.8%)Imbalance
Sex is highly imbalanced (53.5%)Imbalance
State is highly imbalanced (96.9%)Imbalance
Religion is highly imbalanced (86.2%)Imbalance
Relation has 485092 (28.6%) missing valuesMissing
RelationshipName has 509856 (30.1%) missing valuesMissing
Sex has 135307 (8.0%) missing valuesMissing
Occupation has 249829 (14.7%) missing valuesMissing
Age has 162147 (9.6%) zerosZeros

Reproduction

Analysis started2024-04-14 05:56:19.279372
Analysis finished2024-04-14 05:59:29.801224
Duration3 minutes and 10.52 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

District_Name
Categorical

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Bengaluru City
430842 
Bengaluru Dist
 
65041
Tumakuru
 
62528
Shivamogga
 
62053
Mandya
 
60224
Other values (36)
1015704 

Length

Max length23
Median length21
Mean length11.015687
Min length3

Characters and Unicode

Total characters18686924
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBagalkot
2nd rowBagalkot
3rd rowBagalkot
4th rowBagalkot
5th rowBagalkot

Common Values

ValueCountFrequency (%)
Bengaluru City 430842
25.4%
Bengaluru Dist 65041
 
3.8%
Tumakuru 62528
 
3.7%
Shivamogga 62053
 
3.7%
Mandya 60224
 
3.6%
Belagavi Dist 59986
 
3.5%
Hassan 58792
 
3.5%
Mysuru Dist 51267
 
3.0%
Chitradurga 48596
 
2.9%
Ramanagara 44862
 
2.6%
Other values (31) 752201
44.3%

Length

2024-04-14T11:29:30.194153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city 559617
22.2%
bengaluru 496032
19.7%
dist 176294
 
7.0%
mysuru 82165
 
3.3%
belagavi 80968
 
3.2%
tumakuru 62528
 
2.5%
shivamogga 62053
 
2.5%
mandya 60224
 
2.4%
hassan 58792
 
2.3%
kannada 54415
 
2.2%
Other values (33) 828486
32.9%

Most occurring characters

ValueCountFrequency (%)
a 3102442
16.6%
u 1766875
 
9.5%
i 1356923
 
7.3%
r 1355418
 
7.3%
g 1091358
 
5.8%
l 965349
 
5.2%
n 922748
 
4.9%
t 889287
 
4.8%
825182
 
4.4%
y 778174
 
4.2%
Other values (30) 5633168
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18686924
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3102442
16.6%
u 1766875
 
9.5%
i 1356923
 
7.3%
r 1355418
 
7.3%
g 1091358
 
5.8%
l 965349
 
5.2%
n 922748
 
4.9%
t 889287
 
4.8%
825182
 
4.4%
y 778174
 
4.2%
Other values (30) 5633168
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18686924
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3102442
16.6%
u 1766875
 
9.5%
i 1356923
 
7.3%
r 1355418
 
7.3%
g 1091358
 
5.8%
l 965349
 
5.2%
n 922748
 
4.9%
t 889287
 
4.8%
825182
 
4.4%
y 778174
 
4.2%
Other values (30) 5633168
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18686924
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3102442
16.6%
u 1766875
 
9.5%
i 1356923
 
7.3%
r 1355418
 
7.3%
g 1091358
 
5.8%
l 965349
 
5.2%
n 922748
 
4.9%
t 889287
 
4.8%
825182
 
4.4%
y 778174
 
4.2%
Other values (30) 5633168
30.1%
Distinct1057
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
2024-04-14T11:29:30.819943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length44
Median length31
Mean length14.82499
Min length3

Characters and Unicode

Total characters25148994
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAmengad PS
2nd rowAmengad PS
3rd rowAmengad PS
4th rowAmengad PS
5th rowAmengad PS
ValueCountFrequency (%)
ps 1673527
39.3%
rural 157499
 
3.7%
traffic 148361
 
3.5%
town 109854
 
2.6%
crime 72177
 
1.7%
cen 54006
 
1.3%
nagar 51633
 
1.2%
station 23307
 
0.5%
women 22511
 
0.5%
police 21888
 
0.5%
Other values (828) 1919929
45.1%
2024-04-14T11:29:31.994524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3808174
15.1%
2595948
 
10.3%
S 1889872
 
7.5%
P 1767155
 
7.0%
r 1608923
 
6.4%
i 1197751
 
4.8%
n 1042904
 
4.1%
l 1034555
 
4.1%
u 946827
 
3.8%
e 827271
 
3.3%
Other values (44) 8429614
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25148994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3808174
15.1%
2595948
 
10.3%
S 1889872
 
7.5%
P 1767155
 
7.0%
r 1608923
 
6.4%
i 1197751
 
4.8%
n 1042904
 
4.1%
l 1034555
 
4.1%
u 946827
 
3.8%
e 827271
 
3.3%
Other values (44) 8429614
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25148994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3808174
15.1%
2595948
 
10.3%
S 1889872
 
7.5%
P 1767155
 
7.0%
r 1608923
 
6.4%
i 1197751
 
4.8%
n 1042904
 
4.1%
l 1034555
 
4.1%
u 946827
 
3.8%
e 827271
 
3.3%
Other values (44) 8429614
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25148994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3808174
15.1%
2595948
 
10.3%
S 1889872
 
7.5%
P 1767155
 
7.0%
r 1608923
 
6.4%
i 1197751
 
4.8%
n 1042904
 
4.1%
l 1034555
 
4.1%
u 946827
 
3.8%
e 827271
 
3.3%
Other values (44) 8429614
33.5%

Year
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4985
Minimum2016
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-04-14T11:29:32.589521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32022
95-th percentile2023
Maximum2024
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4695878
Coefficient of variation (CV)0.0012228718
Kurtosis-1.3062582
Mean2019.4985
Median Absolute Deviation (MAD)2
Skewness0.1220288
Sum3.4258611 × 109
Variance6.0988641
MonotonicityNot monotonic
2024-04-14T11:29:33.033644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2017 250415
14.8%
2023 234008
13.8%
2016 230633
13.6%
2018 222846
13.1%
2022 197368
11.6%
2021 178041
10.5%
2019 177646
10.5%
2020 162545
9.6%
2024 42890
 
2.5%
ValueCountFrequency (%)
2016 230633
13.6%
2017 250415
14.8%
2018 222846
13.1%
2019 177646
10.5%
2020 162545
9.6%
2021 178041
10.5%
2022 197368
11.6%
2023 234008
13.8%
2024 42890
 
2.5%
ValueCountFrequency (%)
2024 42890
 
2.5%
2023 234008
13.8%
2022 197368
11.6%
2021 178041
10.5%
2020 162545
9.6%
2019 177646
10.5%
2018 222846
13.1%
2017 250415
14.8%
2016 230633
13.6%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2652005
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-04-14T11:29:33.462862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4507391
Coefficient of variation (CV)0.55077873
Kurtosis-1.2145106
Mean6.2652005
Median Absolute Deviation (MAD)3
Skewness0.093694212
Sum10628236
Variance11.907601
MonotonicityNot monotonic
2024-04-14T11:29:33.913715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 160601
9.5%
3 160164
9.4%
1 149497
8.8%
4 146641
8.6%
7 141278
8.3%
6 141062
8.3%
5 139466
8.2%
8 138757
8.2%
9 129977
7.7%
10 129945
7.7%
Other values (2) 259004
15.3%
ValueCountFrequency (%)
1 149497
8.8%
2 160601
9.5%
3 160164
9.4%
4 146641
8.6%
5 139466
8.2%
6 141062
8.3%
7 141278
8.3%
8 138757
8.2%
9 129977
7.7%
10 129945
7.7%
ValueCountFrequency (%)
12 129490
7.6%
11 129514
7.6%
10 129945
7.7%
9 129977
7.7%
8 138757
8.2%
7 141278
8.3%
6 141062
8.3%
5 139466
8.2%
4 146641
8.6%
3 160164
9.4%

Relation
Categorical

IMBALANCE  MISSING 

Distinct34
Distinct (%)< 0.1%
Missing485092
Missing (%)28.6%
Memory size12.9 MiB
Father
919463 
Husband
247929 
Self
 
18397
Wife
 
11913
Son
 
4208
Other values (29)
 
9390

Length

Max length16
Median length6
Mean length6.1544085
Min length3

Characters and Unicode

Total characters7454835
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowFather
2nd rowFather
3rd rowFather
4th rowFather
5th rowFather

Common Values

ValueCountFrequency (%)
Father 919463
54.2%
Husband 247929
 
14.6%
Self 18397
 
1.1%
Wife 11913
 
0.7%
Son 4208
 
0.2%
Mother 3939
 
0.2%
Daughter 3595
 
0.2%
Guardian 514
 
< 0.1%
Brother 293
 
< 0.1%
Friend 231
 
< 0.1%
Other values (24) 818
 
< 0.1%
(Missing) 485092
28.6%

Length

2024-04-14T11:29:34.385536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
father 919485
75.9%
husband 247929
 
20.5%
self 18397
 
1.5%
wife 11913
 
1.0%
son 4214
 
0.3%
mother 3950
 
0.3%
daughter 3720
 
0.3%
guardian 514
 
< 0.1%
brother 293
 
< 0.1%
friend 231
 
< 0.1%
Other values (27) 935
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1172911
15.7%
e 958535
12.9%
r 929102
12.5%
t 928096
12.4%
h 927691
12.4%
F 919858
12.3%
n 253581
 
3.4%
u 252314
 
3.4%
d 248870
 
3.3%
s 248094
 
3.3%
Other values (31) 615783
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7454835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1172911
15.7%
e 958535
12.9%
r 929102
12.5%
t 928096
12.4%
h 927691
12.4%
F 919858
12.3%
n 253581
 
3.4%
u 252314
 
3.4%
d 248870
 
3.3%
s 248094
 
3.3%
Other values (31) 615783
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7454835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1172911
15.7%
e 958535
12.9%
r 929102
12.5%
t 928096
12.4%
h 927691
12.4%
F 919858
12.3%
n 253581
 
3.4%
u 252314
 
3.4%
d 248870
 
3.3%
s 248094
 
3.3%
Other values (31) 615783
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7454835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1172911
15.7%
e 958535
12.9%
r 929102
12.5%
t 928096
12.4%
h 927691
12.4%
F 919858
12.3%
n 253581
 
3.4%
u 252314
 
3.4%
d 248870
 
3.3%
s 248094
 
3.3%
Other values (31) 615783
8.3%

RelationshipName
Text

MISSING 

Distinct509521
Distinct (%)42.9%
Missing509856
Missing (%)30.1%
Memory size12.9 MiB
2024-04-14T11:29:35.715984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length50
Median length45
Mean length11.946316
Min length1

Characters and Unicode

Total characters14174734
Distinct characters93
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique433617 ?
Unique (%)36.5%

Sample

1st rowSHANKRAYYA
2nd rowS
3rd rowR
4th rowSADANAND
5th rowS
ValueCountFrequency (%)
late 179166
 
9.1%
k 26425
 
1.3%
m 23335
 
1.2%
s 23181
 
1.2%
b 17775
 
0.9%
kumar 15830
 
0.8%
r 13316
 
0.7%
n 13290
 
0.7%
h 11906
 
0.6%
g 11086
 
0.6%
Other values (174058) 1640149
83.0%
2024-04-14T11:29:36.990582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1989484
 
14.0%
A 1351919
 
9.5%
798347
 
5.6%
n 479416
 
3.4%
h 454187
 
3.2%
N 434258
 
3.1%
e 421651
 
3.0%
S 413385
 
2.9%
R 411880
 
2.9%
r 393889
 
2.8%
Other values (83) 7026318
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14174734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1989484
 
14.0%
A 1351919
 
9.5%
798347
 
5.6%
n 479416
 
3.4%
h 454187
 
3.2%
N 434258
 
3.1%
e 421651
 
3.0%
S 413385
 
2.9%
R 411880
 
2.9%
r 393889
 
2.8%
Other values (83) 7026318
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14174734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1989484
 
14.0%
A 1351919
 
9.5%
798347
 
5.6%
n 479416
 
3.4%
h 454187
 
3.2%
N 434258
 
3.1%
e 421651
 
3.0%
S 413385
 
2.9%
R 411880
 
2.9%
r 393889
 
2.8%
Other values (83) 7026318
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14174734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1989484
 
14.0%
A 1351919
 
9.5%
798347
 
5.6%
n 479416
 
3.4%
h 454187
 
3.2%
N 434258
 
3.1%
e 421651
 
3.0%
S 413385
 
2.9%
R 411880
 
2.9%
r 393889
 
2.8%
Other values (83) 7026318
49.6%
Distinct9711
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Minimum1900-01-01 00:00:00
Maximum2014-04-06 00:00:00
2024-04-14T11:29:37.510573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T11:29:38.008220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Age
Real number (ℝ)

ZEROS 

Distinct106
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.16596
Minimum0
Maximum119
Zeros162147
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size12.9 MiB
2024-04-14T11:29:38.462192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128
median36
Q347
95-th percentile60
Maximum119
Range119
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.568281
Coefficient of variation (CV)0.4581181
Kurtosis0.28253697
Mean36.16596
Median Absolute Deviation (MAD)9
Skewness-0.44864553
Sum61351645
Variance274.50793
MonotonicityNot monotonic
2024-04-14T11:29:38.900327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 162147
 
9.6%
35 77316
 
4.6%
40 70456
 
4.2%
45 67796
 
4.0%
30 66989
 
3.9%
38 61310
 
3.6%
32 59955
 
3.5%
36 50425
 
3.0%
28 49948
 
2.9%
50 46653
 
2.8%
Other values (96) 983397
58.0%
ValueCountFrequency (%)
0 162147
9.6%
1 155
 
< 0.1%
2 20
 
< 0.1%
3 41
 
< 0.1%
4 23
 
< 0.1%
5 22
 
< 0.1%
6 10
 
< 0.1%
7 6
 
< 0.1%
8 7
 
< 0.1%
9 25
 
< 0.1%
ValueCountFrequency (%)
119 1
 
< 0.1%
111 1
 
< 0.1%
110 1
 
< 0.1%
105 3
< 0.1%
102 3
< 0.1%
101 2
 
< 0.1%
100 4
< 0.1%
98 6
< 0.1%
97 5
< 0.1%
96 6
< 0.1%

Sex
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing135307
Missing (%)8.0%
Memory size12.9 MiB
MALE
1239548 
FEMALE
321082 
Enuch
 
455

Length

Max length6
Median length4
Mean length4.4116489
Min length4

Characters and Unicode

Total characters6886959
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
MALE 1239548
73.1%
FEMALE 321082
 
18.9%
Enuch 455
 
< 0.1%
(Missing) 135307
 
8.0%

Length

2024-04-14T11:29:39.260178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T11:29:39.635434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
male 1239548
79.4%
female 321082
 
20.6%
enuch 455
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 1882167
27.3%
M 1560630
22.7%
A 1560630
22.7%
L 1560630
22.7%
F 321082
 
4.7%
n 455
 
< 0.1%
u 455
 
< 0.1%
c 455
 
< 0.1%
h 455
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6886959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1882167
27.3%
M 1560630
22.7%
A 1560630
22.7%
L 1560630
22.7%
F 321082
 
4.7%
n 455
 
< 0.1%
u 455
 
< 0.1%
c 455
 
< 0.1%
h 455
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6886959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1882167
27.3%
M 1560630
22.7%
A 1560630
22.7%
L 1560630
22.7%
F 321082
 
4.7%
n 455
 
< 0.1%
u 455
 
< 0.1%
c 455
 
< 0.1%
h 455
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6886959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1882167
27.3%
M 1560630
22.7%
A 1560630
22.7%
L 1560630
22.7%
F 321082
 
4.7%
n 455
 
< 0.1%
u 455
 
< 0.1%
c 455
 
< 0.1%
h 455
 
< 0.1%
Distinct105
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
2024-04-14T11:29:39.994800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length30
Median length5
Mean length5.001904
Min length3

Characters and Unicode

Total characters8485190
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)< 0.1%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia
ValueCountFrequency (%)
india 1695192
99.9%
indonesia 288
 
< 0.1%
haiti 150
 
< 0.1%
iran 115
 
< 0.1%
nepal 115
 
< 0.1%
united 54
 
< 0.1%
states 37
 
< 0.1%
of 37
 
< 0.1%
america 37
 
< 0.1%
usa 37
 
< 0.1%
Other values (114) 564
 
< 0.1%
2024-04-14T11:29:40.620040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1696561
20.0%
n 1696276
20.0%
i 1696135
20.0%
d 1695702
20.0%
I 1695690
20.0%
e 784
 
< 0.1%
s 443
 
< 0.1%
o 426
 
< 0.1%
r 391
 
< 0.1%
t 388
 
< 0.1%
Other values (43) 2394
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8485190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1696561
20.0%
n 1696276
20.0%
i 1696135
20.0%
d 1695702
20.0%
I 1695690
20.0%
e 784
 
< 0.1%
s 443
 
< 0.1%
o 426
 
< 0.1%
r 391
 
< 0.1%
t 388
 
< 0.1%
Other values (43) 2394
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8485190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1696561
20.0%
n 1696276
20.0%
i 1696135
20.0%
d 1695702
20.0%
I 1695690
20.0%
e 784
 
< 0.1%
s 443
 
< 0.1%
o 426
 
< 0.1%
r 391
 
< 0.1%
t 388
 
< 0.1%
Other values (43) 2394
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8485190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1696561
20.0%
n 1696276
20.0%
i 1696135
20.0%
d 1695702
20.0%
I 1695690
20.0%
e 784
 
< 0.1%
s 443
 
< 0.1%
o 426
 
< 0.1%
r 391
 
< 0.1%
t 388
 
< 0.1%
Other values (43) 2394
 
< 0.1%

Occupation
Text

MISSING 

Distinct175
Distinct (%)< 0.1%
Missing249829
Missing (%)14.7%
Memory size12.9 MiB
2024-04-14T11:29:41.182775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length41
Median length27
Mean length12.615871
Min length3

Characters and Unicode

Total characters18249652
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowLabourer
2nd rowPolice officer
3rd rowPolice officer
4th rowBusinessman
5th rowTeacher
ValueCountFrequency (%)
officer 431136
18.0%
police 429577
17.9%
farmer 186137
 
7.8%
labourer 146893
 
6.1%
housewife 135454
 
5.6%
others 112274
 
4.7%
pi 101867
 
4.2%
specify 101867
 
4.2%
businessman 76487
 
3.2%
driver 53245
 
2.2%
Other values (210) 623743
26.0%
2024-04-14T11:29:42.293533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2323441
12.7%
2175403
11.9%
r 1652018
 
9.1%
i 1537305
 
8.4%
o 1446194
 
7.9%
f 1225583
 
6.7%
c 1111957
 
6.1%
a 679110
 
3.7%
l 597118
 
3.3%
s 593207
 
3.3%
Other values (44) 4908316
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18249652
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2323441
12.7%
2175403
11.9%
r 1652018
 
9.1%
i 1537305
 
8.4%
o 1446194
 
7.9%
f 1225583
 
6.7%
c 1111957
 
6.1%
a 679110
 
3.7%
l 597118
 
3.3%
s 593207
 
3.3%
Other values (44) 4908316
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18249652
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2323441
12.7%
2175403
11.9%
r 1652018
 
9.1%
i 1537305
 
8.4%
o 1446194
 
7.9%
f 1225583
 
6.7%
c 1111957
 
6.1%
a 679110
 
3.7%
l 597118
 
3.3%
s 593207
 
3.3%
Other values (44) 4908316
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18249652
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2323441
12.7%
2175403
11.9%
r 1652018
 
9.1%
i 1537305
 
8.4%
o 1446194
 
7.9%
f 1225583
 
6.7%
c 1111957
 
6.1%
a 679110
 
3.7%
l 597118
 
3.3%
s 593207
 
3.3%
Other values (44) 4908316
26.9%
Distinct1203026
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
2024-04-14T11:29:47.697794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length201
Median length180
Mean length43.347351
Min length1

Characters and Unicode

Total characters73534100
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1095156 ?
Unique (%)64.6%

Sample

1st rowHUVINAHALLI,TQ-HUNAGUND
2nd rowAMINAGAD PS,TQ-HUNAGUND
3rd rowAMINAGAD PS,TQ-HUNAGUND
4th rowBASAVA NAGAR GOKAK CTS NO 190/5 PLAT NO 02,TQ-GOKAK
5th rowAMBLIKOPPA NOW AT ILKAL,TQ-HUNAGUND
ValueCountFrequency (%)
police 319753
 
3.6%
tq 262198
 
3.0%
taluk 168599
 
1.9%
cross 144811
 
1.6%
no 142166
 
1.6%
town 139788
 
1.6%
village 128907
 
1.5%
road 118778
 
1.3%
main 116235
 
1.3%
nagar 80823
 
0.9%
Other values (806359) 7184856
81.6%
2024-04-14T11:29:50.034373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7218402
 
9.8%
a 6444751
 
8.8%
A 4242182
 
5.8%
, 2958007
 
4.0%
l 2625703
 
3.6%
i 2503329
 
3.4%
r 2137711
 
2.9%
o 2096186
 
2.9%
T 2026274
 
2.8%
N 2015329
 
2.7%
Other values (78) 39266226
53.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73534100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7218402
 
9.8%
a 6444751
 
8.8%
A 4242182
 
5.8%
, 2958007
 
4.0%
l 2625703
 
3.6%
i 2503329
 
3.4%
r 2137711
 
2.9%
o 2096186
 
2.9%
T 2026274
 
2.8%
N 2015329
 
2.7%
Other values (78) 39266226
53.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73534100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7218402
 
9.8%
a 6444751
 
8.8%
A 4242182
 
5.8%
, 2958007
 
4.0%
l 2625703
 
3.6%
i 2503329
 
3.4%
r 2137711
 
2.9%
o 2096186
 
2.9%
T 2026274
 
2.8%
N 2015329
 
2.7%
Other values (78) 39266226
53.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73534100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7218402
 
9.8%
a 6444751
 
8.8%
A 4242182
 
5.8%
, 2958007
 
4.0%
l 2625703
 
3.6%
i 2503329
 
3.4%
r 2137711
 
2.9%
o 2096186
 
2.9%
T 2026274
 
2.8%
N 2015329
 
2.7%
Other values (78) 39266226
53.4%

City
Text

Distinct643
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
2024-04-14T11:29:50.519249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length29
Median length24
Mean length10.873792
Min length3

Characters and Unicode

Total characters18446213
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)< 0.1%

Sample

1st rowBagalkot
2nd rowBagalkot
3rd rowBagalkot
4th rowBelagavi Dist
5th rowBagalkot
ValueCountFrequency (%)
city 557654
22.3%
bengaluru 499067
19.9%
dist 170585
 
6.8%
mysuru 81903
 
3.3%
belagavi 80453
 
3.2%
shivamogga 61799
 
2.5%
tumakuru 60695
 
2.4%
mandya 59549
 
2.4%
hassan 58534
 
2.3%
kannada 53768
 
2.1%
Other values (637) 821304
32.8%
2024-04-14T11:29:51.536415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2970925
16.1%
u 1770877
 
9.6%
r 1351967
 
7.3%
i 1345710
 
7.3%
g 1071102
 
5.8%
l 993582
 
5.4%
n 902578
 
4.9%
t 875328
 
4.7%
808969
 
4.4%
y 748748
 
4.1%
Other values (46) 5606427
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18446213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2970925
16.1%
u 1770877
 
9.6%
r 1351967
 
7.3%
i 1345710
 
7.3%
g 1071102
 
5.8%
l 993582
 
5.4%
n 902578
 
4.9%
t 875328
 
4.7%
808969
 
4.4%
y 748748
 
4.1%
Other values (46) 5606427
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18446213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2970925
16.1%
u 1770877
 
9.6%
r 1351967
 
7.3%
i 1345710
 
7.3%
g 1071102
 
5.8%
l 993582
 
5.4%
n 902578
 
4.9%
t 875328
 
4.7%
808969
 
4.4%
y 748748
 
4.1%
Other values (46) 5606427
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18446213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2970925
16.1%
u 1770877
 
9.6%
r 1351967
 
7.3%
i 1345710
 
7.3%
g 1071102
 
5.8%
l 993582
 
5.4%
n 902578
 
4.9%
t 875328
 
4.7%
808969
 
4.4%
y 748748
 
4.1%
Other values (46) 5606427
30.4%

State
Categorical

IMBALANCE 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Karnataka
1671529 
Andhra pradesh
 
5503
Maharashtra
 
5496
Tamilnadu
 
4818
Kerala
 
2428
Other values (31)
 
6618

Length

Max length20
Median length9
Mean length9.0175136
Min length3

Characters and Unicode

Total characters15297238
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKarnataka
2nd rowKarnataka
3rd rowKarnataka
4th rowKarnataka
5th rowKarnataka

Common Values

ValueCountFrequency (%)
Karnataka 1671529
98.5%
Andhra pradesh 5503
 
0.3%
Maharashtra 5496
 
0.3%
Tamilnadu 4818
 
0.3%
Kerala 2428
 
0.1%
Telangana 1074
 
0.1%
Uttar pradesh 947
 
0.1%
Bihar 684
 
< 0.1%
West bengal 563
 
< 0.1%
Rajasthan 525
 
< 0.1%
Other values (26) 2825
 
0.2%

Length

2024-04-14T11:29:52.099097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
karnataka 1671529
98.1%
pradesh 6846
 
0.4%
andhra 5503
 
0.3%
maharashtra 5496
 
0.3%
tamilnadu 4818
 
0.3%
kerala 2428
 
0.1%
telangana 1074
 
0.1%
uttar 947
 
0.1%
bihar 684
 
< 0.1%
west 563
 
< 0.1%
Other values (36) 4080
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 6745959
44.1%
r 1700389
 
11.1%
n 1685856
 
11.0%
t 1680618
 
11.0%
K 1673957
 
10.9%
k 1671853
 
10.9%
h 26330
 
0.2%
d 17878
 
0.1%
s 14684
 
0.1%
e 11879
 
0.1%
Other values (32) 67835
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15297238
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6745959
44.1%
r 1700389
 
11.1%
n 1685856
 
11.0%
t 1680618
 
11.0%
K 1673957
 
10.9%
k 1671853
 
10.9%
h 26330
 
0.2%
d 17878
 
0.1%
s 14684
 
0.1%
e 11879
 
0.1%
Other values (32) 67835
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15297238
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6745959
44.1%
r 1700389
 
11.1%
n 1685856
 
11.0%
t 1680618
 
11.0%
K 1673957
 
10.9%
k 1671853
 
10.9%
h 26330
 
0.2%
d 17878
 
0.1%
s 14684
 
0.1%
e 11879
 
0.1%
Other values (32) 67835
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15297238
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6745959
44.1%
r 1700389
 
11.1%
n 1685856
 
11.0%
t 1680618
 
11.0%
K 1673957
 
10.9%
k 1671853
 
10.9%
h 26330
 
0.2%
d 17878
 
0.1%
s 14684
 
0.1%
e 11879
 
0.1%
Other values (32) 67835
 
0.4%

Caste
Text

Distinct967
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
2024-04-14T11:29:52.803815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length52
Median length9
Mean length8.5856671
Min length3

Characters and Unicode

Total characters14564657
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)< 0.1%

Sample

1st rowLingayath
2nd rowLingayath
3rd rowLingayath
4th rowBRAHMIN
5th rowGANIGA
ValueCountFrequency (%)
lingayath 878837
47.2%
vokkaliga 112323
 
6.0%
muslim 107639
 
5.8%
adi 77724
 
4.2%
karnataka 66097
 
3.5%
kuruba 35859
 
1.9%
nayaka 29470
 
1.6%
brahmin 24662
 
1.3%
lambani 24165
 
1.3%
christian 18056
 
1.0%
Other values (1049) 487795
26.2%
2024-04-14T11:29:54.041356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1796915
12.3%
A 1511558
 
10.4%
L 1262734
 
8.7%
i 890288
 
6.1%
y 889299
 
6.1%
t 886755
 
6.1%
n 884613
 
6.1%
h 883179
 
6.1%
g 877870
 
6.0%
I 594792
 
4.1%
Other values (45) 4086654
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14564657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1796915
12.3%
A 1511558
 
10.4%
L 1262734
 
8.7%
i 890288
 
6.1%
y 889299
 
6.1%
t 886755
 
6.1%
n 884613
 
6.1%
h 883179
 
6.1%
g 877870
 
6.0%
I 594792
 
4.1%
Other values (45) 4086654
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14564657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1796915
12.3%
A 1511558
 
10.4%
L 1262734
 
8.7%
i 890288
 
6.1%
y 889299
 
6.1%
t 886755
 
6.1%
n 884613
 
6.1%
h 883179
 
6.1%
g 877870
 
6.0%
I 594792
 
4.1%
Other values (45) 4086654
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14564657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1796915
12.3%
A 1511558
 
10.4%
L 1262734
 
8.7%
i 890288
 
6.1%
y 889299
 
6.1%
t 886755
 
6.1%
n 884613
 
6.1%
h 883179
 
6.1%
g 877870
 
6.0%
I 594792
 
4.1%
Other values (45) 4086654
28.1%

Religion
Categorical

IMBALANCE 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Hindu
1547556 
Muslim
 
119850
Christian
 
20503
Jain
 
7410
Sikh
 
699
Other values (8)
 
374

Length

Max length12
Median length6
Mean length6.1148538
Min length5

Characters and Unicode

Total characters10373189
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowHindu
2nd rowHindu
3rd rowHindu
4th rowHindu
5th rowHindu

Common Values

ValueCountFrequency (%)
Hindu 1547556
91.2%
Muslim 119850
 
7.1%
Christian 20503
 
1.2%
Jain 7410
 
0.4%
Sikh 699
 
< 0.1%
Buddhist 287
 
< 0.1%
Parsi 34
 
< 0.1%
Donyipolo 31
 
< 0.1%
Jews/yehudi 15
 
< 0.1%
BUDDHISTS 3
 
< 0.1%
Other values (3) 4
 
< 0.1%

Length

2024-04-14T11:29:54.509551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hindu 1547556
91.2%
muslim 119850
 
7.1%
christian 20503
 
1.2%
jain 7410
 
0.4%
sikh 699
 
< 0.1%
buddhist 287
 
< 0.1%
parsi 34
 
< 0.1%
donyipolo 31
 
< 0.1%
jews/yehudi 15
 
< 0.1%
buddhists 3
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 1716888
16.6%
1696385
16.4%
u 1667708
16.1%
n 1575500
15.2%
d 1548147
14.9%
H 1547560
14.9%
s 140691
 
1.4%
l 119883
 
1.2%
M 119850
 
1.2%
m 119850
 
1.2%
Other values (23) 120727
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10373189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1716888
16.6%
1696385
16.4%
u 1667708
16.1%
n 1575500
15.2%
d 1548147
14.9%
H 1547560
14.9%
s 140691
 
1.4%
l 119883
 
1.2%
M 119850
 
1.2%
m 119850
 
1.2%
Other values (23) 120727
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10373189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1716888
16.6%
1696385
16.4%
u 1667708
16.1%
n 1575500
15.2%
d 1548147
14.9%
H 1547560
14.9%
s 140691
 
1.4%
l 119883
 
1.2%
M 119850
 
1.2%
m 119850
 
1.2%
Other values (23) 120727
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10373189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1716888
16.6%
1696385
16.4%
u 1667708
16.1%
n 1575500
15.2%
d 1548147
14.9%
H 1547560
14.9%
s 140691
 
1.4%
l 119883
 
1.2%
M 119850
 
1.2%
m 119850
 
1.2%
Other values (23) 120727
 
1.2%

Interactions

2024-04-14T11:29:05.167414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T11:29:01.174350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T11:29:03.240511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T11:29:05.826030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T11:29:01.988986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T11:29:03.869689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T11:29:06.467624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T11:29:02.631138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-14T11:29:04.526116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-04-14T11:29:08.530918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T11:29:12.867644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

District_NameUnitNameYearMonthRelationRelationshipNameDateOfBirthAgeSexNationalityOccupationAddressCityStateCasteReligion
0BagalkotAmengad PS20161FatherSHANKRAYYA1900-01-01 00:00:00.00040MALEIndiaLabourerHUVINAHALLI,TQ-HUNAGUNDBagalkotKarnatakaLingayathHindu
1BagalkotAmengad PS20161FatherS1900-01-01 00:00:00.00048MALEIndiaPolice officerAMINAGAD PS,TQ-HUNAGUNDBagalkotKarnatakaLingayathHindu
2BagalkotAmengad PS20161FatherR1900-01-01 00:00:00.00059MALEIndiaPolice officerAMINAGAD PS,TQ-HUNAGUNDBagalkotKarnatakaLingayathHindu
3BagalkotAmengad PS20161FatherSADANAND1900-01-01 00:00:00.00040MALEIndiaBusinessmanBASAVA NAGAR GOKAK CTS NO 190/5 PLAT NO 02,TQ-GOKAKBelagavi DistKarnatakaBRAHMINHindu
4BagalkotAmengad PS20161NaNNaN1900-01-01 00:00:00.0000NaNIndiaTeacherAMBLIKOPPA NOW AT ILKAL,TQ-HUNAGUNDBagalkotKarnatakaGANIGAHindu
5BagalkotAmengad PS20161NaNNaN1900-01-01 00:00:00.00027NaNIndiaBusinessmanMUTAGA,TQ-BELAGAVIBelagavi CityKarnatakaMARATHAHindu
6BagalkotAmengad PS20161NaNNaN1900-01-01 00:00:00.00052NaNIndiaPolice officerDCB PS BAGALKOT,BAGALKOTBagalkotKarnatakaKURABHindu
7BagalkotAmengad PS20161NaNNaN1900-01-01 00:00:00.00052NaNIndiaPolice officerDCB PS BAGALKOT,TQ-BAGALKOTBagalkotKarnatakaKURABHindu
8BagalkotAmengad PS20161NaNNaN1900-01-01 00:00:00.00048NaNIndiaPolice officerAMINAGAD PS,TQ-HUNGUNDBagalkotKarnatakaLingayathHindu
9BagalkotAmengad PS20161FatherS1900-01-01 00:00:00.00048MALEIndiaPolice officerAMINAGADA PS,TQ-HUNAGUNDBagalkotKarnatakaLingayathHindu
District_NameUnitNameYearMonthRelationRelationshipNameDateOfBirthAgeSexNationalityOccupationAddressCityStateCasteReligion
1696382YadgirYadgiri Women PS202311HusbandSabayya1900-01-01 00:00:00.00038NaNIndiaLabourerR/o Thanagundi,Tq Dist YadgiriYadgirKarnatakaBEDARUHindu
1696383YadgirYadgiri Women PS202311HusbandParashuram1900-01-01 00:00:00.00031FEMALEIndiaNurseR/o Naykal,Now At Mata Manikeshwari Nagara YadgiriYadgirKarnatakaMADIGAHindu
1696384YadgirYadgiri Women PS202311FatherMallayya1900-01-01 00:00:00.00050NaNIndiaFarmerR/o M Hosalli,Tq Dist YadgiriYadgirKarnatakaBEDARUHindu
1696385YadgirYadgiri Women PS202312HusbandSuresh1900-01-01 00:00:00.00030NaNIndiaNGOsR/o Kotagarawada,YADAGIRYadgirKarnatakaHOLAYA, HOLER, HOLEYAHindu
1696386YadgirYadgiri Women PS20241FatherGANGARAM RATHOD1900-01-01 00:00:00.00022FEMALEIndiaLabourerSAMANAPURA SANNA THANDA,YADAGIRYadgirKarnatakaLAMBANIHindu
1696387YadgirYadgiri Women PS20241FatherSimeon1900-01-01 00:00:00.00058MALEIndiaPublic Sector UndertakingR/o Hosalli Cross Near Ratanam School,yadgiriYadgirKarnatakaLingayathChristian
1696388YadgirYadgiri Women PS20241FatherSabanna Sadoor1900-01-01 00:00:00.00055MALEIndiaLabourer,YadgirKarnatakaHOLAYA, HOLER, HOLEYAHindu
1696389YadgirYadgiri Women PS20242HusbandSharbanna Mathpalli1900-01-01 00:00:00.00029FEMALEIndiaTeacherR/o Bilahar Village,tq wadageri dist yadgirYadgirKarnatakaLingayathHindu
1696390YadgirYadgiri Women PS20242HusbandSharnabasava Sahubangari1900-01-01 00:00:00.00029FEMALEIndiaHouse help - hiredR/o Thanagundi Village,now at Oppoiste Mini Vidansouda yadgiriYadgirKarnatakaREDDYHindu
1696391YadgirYadgiri Women PS20242HusbandDurgappa1900-01-01 00:00:00.00045NaNIndiaLabourerR/o Bandalli,Tq Dist YadgirYadgirKarnatakaKABBALIGAHindu

Duplicate rows

Most frequently occurring

District_NameUnitNameYearMonthRelationRelationshipNameDateOfBirthAgeSexNationalityOccupationAddressCityStateCasteReligion# duplicates
25086DavanagereDavanagere Extention PS20177NaNNaN1900-01-01 00:00:00.0000FEMALEIndiaPolice officerPolice Sub Inspector,Extention Police StationDavanagereKarnatakaLingayathHindu127
7665Bengaluru CityByatrarayanapura Traffic PS201612NaNNaN1900-01-01 00:00:00.0000NaNIndiaPolice officerByatarayanapura traffic ps,Mysore roadBengaluru CityKarnatakaLingayathHindu118
46365ShivamoggaThirthahalli PS20182NaNNaN1900-01-01 00:00:00.00034MALEIndiaPolice officerTHIRTHAHALLI POLICE STATION,THIRTHAHALLIShivamoggaKarnatakaLingayathHindu109
18389ChamarajanagarChamarajanagar Town PS20184FatherNaN1900-01-01 00:00:00.00032MALEIndiaPolice officerPSI, Town police station,CH NAGARA TOWNChamarajanagarKarnatakaLingayathHindu103
40594Mysuru DistHunusur Town PS20184NaNNaN1900-01-01 00:00:00.00044NaNIndiaNaNPSI Hunsur Town PS,HUNSUR TOWNMysuru DistKarnatakaLingayathHindu97
46372ShivamoggaThirthahalli PS20182NaNNaN1900-01-01 00:00:00.00034MALEIndiaNaNTHIRTHAHALLI POLICE STATION,THIRTHAHALLI TQShivamoggaKarnatakaLingayathHindu95
7659Bengaluru CityByatrarayanapura Traffic PS201611NaNNaN1900-01-01 00:00:00.0000NaNIndiaPolice officerByatarayanapura traffic ps,Mysore roadBengaluru CityKarnatakaLingayathHindu87
40522Mysuru DistHunusur Rural PS20184NaNNaN1900-01-01 00:00:00.00036MALEIndiaPolice officerHunsur Rural Police Station,Hunsur tqMysuru DistKarnatakaLingayathHindu86
7644Bengaluru CityByatrarayanapura Traffic PS20168NaNNaN1900-01-01 00:00:00.0000NaNIndiaPolice officerByatarayanapura traffic ps,Mysore roadBengaluru CityKarnatakaLingayathHindu85
5783Belagavi DistSadalaga PS20182NaNNaN1900-01-01 00:00:00.00038MALEIndiaNaNSADALAGA,TQ;CHIKODIBelagavi DistKarnatakaLingayathHindu78